Elektron mikroskobu görüntülerı üzerinde kalman filtreleme yaklaşımını kullanılarak yarı-otomatik mitokondri bölütleme.

Mitochondria are membrane bound organelles found in most eukaryotic cells. Mitochondria provide cell’s energy; hence they are called ‘power houses of the cell’. The structure of mitochondria can be illustrated in an electron micrograph. This structure has two membranes: inner and outer. There is a gap between these two membranes, called inter-membrane space. Folds of inner membrane inside the mitochondria form the cristae. To study the relation between mitochondria’s physical structure and its function, electron microscope tomography (EMT) is used to visualize mitochondria. EMT provides 3D structure of mitochondria in high resolution images. In the slices of tomographic images provided by EMT, mitochondria appear as elliptical structures. The cristae are also visualized in these images with various pathology and biological variations. One of the preferred method can be semi-automatic segmentation; since manual segmentation in medical images is time and energy consuming and tedious; moreover fully automatic methods also fail in medical images and cause incorrect results because of low quality of images and restrictions imposed by image acquisition. In this work, an endeavour is made to segment mitochondrial outer boundary using active contour, Kalman filter and optical flow. In the first slice of the images, a contour is provided by user. Then, for the other slices, position values and velocity values calculated using the active contour and optical flow (respectively) are combined with the Kalman filter to predict the points of the boundary in the next slice. In addition, a set of automatic and semi-automatic tools are developed to determine splitting and merging mitochondria, and to segment them.M.S. - Master of Scienc

Assessment of Maternal General Health and Feeding Pattern of their Infants: A Cross-sectional Study in Iranian Population

Background Various studies have been conducted so far regarding feeding infants exclusively with natural mother milk; various factors, which may influence feeding process, are occupation of mothers, sickness of infants or mother. This study aimed to assess the relationship between maternal general health and feeding pattern of infants. Materials and Methods This cross-sectional study was done in 2016. The society in this study was mothers with infants between 0-6 months referred to Imam Ali hospital (Sari, Iran). Using accessible sampling method, 250 mothers were selected. Then they were divided to two groups of dry-powder-milk (DPM, n = 125), and mother-milk (MM, n = 125). Subsequently, general health of mothers in both groups was assessed via General Health Questionnaire (GHQ). Then data were analyzed using the SPSS 16.0 (SPSS Inc., Chicago, IL, USA) by descriptive and inferential statistics tests. Results Mean age of mothers in dry-powder-milk (DPM) group, and mother-milk (MM) group was 28.53 ± 5.44 and 27.7 ± 5.15 years old, respectively. In overall results, general health were not significant difference between the two groups (p = 0.34), but only one dimension of general health (Social functioning) had significant differences between MM and DPM groups (p = 0.02). Conclusion Based on results, only one dimension of general health (Social functioning) had significant differences between two groups. So only social functioning had significant relation with feeding pattern of infants. Also, number of children had significant relationship with total score of GHQ. More studies are recommended to earn more detailed results

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran’s neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff’s SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city’s downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R2 of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO2, PM10, and O3); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran’s neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff’s SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city’s downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R2 of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO2, PM10, and O3); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide

Trehalose: A promising new treatment for traumatic brain injury? A systematic review of animal evidence

Background: TBI is a major global health issue due to its high morbidity and mortality rates. Persistent neurodegeneration following secondary brain injuries is a significant concern. Trehalose, a naturally occurring disaccharide, has shown potential therapeutic effects in preclinical TBI models. This study systematically reviews the preclinical and clinical data on trehalose as a potential TBI treatment. Methods: We conducted a systematic review of trehalose’s role in TBI treatment following PRISMA guidelines. Our search spanned from the inception of PubMed, EMBASE, SCOPUS, and Web of Science until August 2023. Google Scholar was also manually searched. The quality of the studies was assessed using SYRCLE’s risk of bias tool for animal studies. Results: Out of sixty-six records reviewed, four animal studies were included. These studies indicated that trehalose enhanced motor and cognitive functions, reduced oxidative damage and inflammation, regulated metal dyshomeostasis, increased neurotrophic factors and synaptic proteins, and improved autophagy and mitochondrial function in mouse/rat TBI models. However, a significant risk of bias was noted. Conclusion: Trehalose demonstrates potential as a TBI treatment. However, more rigorous, and comprehensive research is needed to confirm its safety and efficacy in humans